Flight-related health effects are a growing area of environmental health research with most work examining the concurrent impact of in-flight exposure on cardiac health. One understudied area is on the post-flight effects of in-flight exposures. Studies on the health effects of flight often collect a range of repeatedly sampled, time-varying exposure measurements both under crossover and longitudinal sampling designs. A natural choice to model the relationship of these lagged exposures on post-flight outcomes is the distributed lag model (DLM). However, longitudinal DLMs are a lightly studied class of models. In this article, we propose a class of models for analyzing longitudinal DLMs where the random effects can incorporate more general structures including random lags that arise from repeatedly sampling lagged exposures. We develop variational Bayesian algorithms to estimate model components under differing random effect structures, derive a variational AIC for model selection between these structures, and show how the converged variational estimates can fit into a framework for testing for the difference between two semiparametric curves. We then investigate the post-flight effects of in-flight, lagged exposures on heart health. We also perform simulation studies to evaluate the operating characteristics of our models.
翻译:与飞行有关的健康影响是环境健康研究的一个日益扩大的领域,大多数工作是审查飞行中接触对心脏健康的同时影响。一个研究不足的领域是飞行中接触对飞行后影响的影响。飞行对健康的影响研究经常收集一系列反复抽样、时间变化的接触测量,在交叉和纵向抽样设计下进行。在飞行后结果中,模拟这些滞后接触关系的自然选择是分布式滞后模型(DLM)。但是,纵向DLMS是经过轻度研究的模型类别。在本篇文章中,我们建议了一组模型,用于分析纵向DLMS的纵向DLMS,随机影响可以包括更一般的结构,包括反复取样滞后的接触产生的随机滞后。我们开发了变异的巴伊斯算法,以估计不同随机效应结构下的模型组成部分,得出这些结构之间模型选择的变异性AIC,并表明聚合的变异性估计如何适合测试两个半分辨曲线之间的差异。我们随后对飞行后飞行后对心脏健康的影响进行了调查。我们还进行了模拟研究。